code: def interpret(
      self,
      X_train,
      y_train,
      X_validation,
      y_validation,
      model_file_path,
      learner_name,
      target_name=None,
      class_names=None,
      metric_name=None,
      ml_task=None,
      explain_level=2,
  ):
      super(DecisionTreeAlgorithm, self).interpret(
          X_train,
          y_train,
          X_validation,
          y_validation,
          model_file_path,
          learner_name,
          target_name,
          class_names,
          metric_name,
          ml_task,
          explain_level,
      )
      if explain_level == 0:
          return
      with warnings.catch_warnings():
          warnings.simplefilter(action="ignore")
          try:
              if len(class_names) > 10:
                  # dtreeviz does not support more than 10 classes
                  return
  
              viz = dtreeviz.model(
                  self.model,
                  X_train,
                  y_train,
                  target_name="target",
                  feature_names=X_train.columns,
                  class_names=class_names,
              )
              tree_file_plot = os.path.join(
                  model_file_path, learner_name + "_tree.svg"
              )
              viz.view().save(tree_file_plot)
          except Exception as e:
              logger.info(
                  f"Problem when visualizing decision tree. {str(e)}"
              )
  
          save_rules(
              self.model,
              X_train.columns,
              class_names,
              model_file_path,
              learner_name,
          )
  
imports: import logging
  import os
  import warnings
  import numpy
  import sklearn
  from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from supervised.algorithms.algorithm import BaseAlgorithm
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION, AlgorithmsRegistry
  from supervised.algorithms.sklearn import SklearnAlgorithm
  from supervised.utils.config import LOG_LEVEL
  import dtreeviz
  from sklearn.tree import _tree
  from supervised.utils.subsample import subsample
label_desc: Use image node_id 3 for calling the DecisionTreeAlgorithm obj's underlying member method code with example usage: obj.interpret(X_train, y_train, X_validation, y_validation, model_file_path, learner_name, target_name, class_names, metric_name, ml_task, explain_level) without return types